Quality assurance/quality control for electrospray ionization processes

ABSTRACT

The present invention relates to a method of quality assurance/quality control for high-throughput bioassay processes. The method includes generating a bioassay process model, and then comparing spectral data based on a combination of a biochip and a test serum to the bioassay process model to determine if the test sample and the bioassay process are producing acceptable data. Alternatively, the method may include comparing spectral data based on a combination of serum and diluents used in an electrospray process to the bioassay process model. If the bioassay process and test sample fall within the model, then the spectrum produced may be further analyzed.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 60/389,831, entitled “QualityAssurance/Quality Control for SELDI-TOF Mass Spectra,” filed on Jul. 29,2002, the contents of which are hereby incorporated by reference in itsentirety.

STATEMENT OF FEDERALLY SPONSORED RESEARCH

[0002] The research work described here was supported under aCooperative Research and Development Agreement (CRADA) between the USGovernment and Correlogic Systems, Inc.

BACKGROUND OF THE INVENTION

[0003] The present invention relates generally to the field ofbioinformatics. More specifically, the present invention relates to amethod of quality assurance/quality control (“QA/QC”) for bioinformaticsystems.

[0004] Methods of analyzing biological samples are generally known. In atypical analysis, mass spectroscopy is performed on the biologicalsample to determine its overall biochemical make-up. Based on the massspectra obtained from the mass spectroscopy, various diagnostics may berun.

[0005] When biological samples are analyzed, it is desirable to run morethan one trial on the biological sample, thereby improving the accuracyof the diagnostic. Analysis of biological samples may be performed byusing various high-throughput mass spectrometry related bioassayprocesses. A process can include using matrix assisted laser desorptionionization time-of-flight (MALDI-TOF) or electrospray techniques (i.e.,generation of droplets by applying a high voltage to a stream ofliquid). When performing multiple mass spectral analyses on the samesample, however, the spectra obtained can vary. This variation may bedue to the mass spectrometer itself, from inconsistencies in the sample,heterogeneity in the patient population, or in sample handling andprocessing. A process that employed a protein chip or surface enhancedtype of mass spectrometry (SELDI-TOF) indicated that various chipsyielded spectra that were inconsistent with one another. Similar effectswere observed with respect to spectra obtained using electrospraytechniques. This inconsistency can lead to inaccurate results whenrunning a diagnostic.

SUMMARY OF THE INVENTION

[0006] The present invention provides a QA/QC method for filtering outinconsistencies across high-throughput bioassay processes, particularlyacross different biochips and different diluents or concentrations ofdiluents used in electrospray techniques.

[0007] The present invention uses the Knowledge Discovery Engine (“KDE”)to identify hidden patterns across a wide variety of serum samples andbiochips to generate a control model and agnostic to the underlyingdisease processes in question. Electrospray, MALDI-TOF (Matrix AssistedLaser Desorption/Ionization-Time of Flight) mass spectra, or SELDI-TOF(Surface Enhanced Laser Desorption/Ionization-Time of Flight) massspectra can be analyzed in this manner, for example. Alternatively, theinvention may use the KDE to identify hidden patterns across a varietyof serum to diluent concentrations to generate a control model. In yetanother embodiment, the KDE may be used to identify hidden patternsacross a variety of diluents and sera samples to generate a controlmodel.

[0008] The KDE is disclosed in U.S. patent application Ser. No.09/883,196, now U.S. application Publication Ser. No. 2002/0,046,198A1,entitled “Heuristic Methods of Classification,” filed Jun. 19, 2001(“Heuristic Methods”), and U.S. patent application Ser. No. 09/906,661,now U.S. application Publication Ser. No. 2003/0,004,402A1, entitled “AProcess for Discriminating Between Biological States Based on HiddenPatterns from Biological Data,” filed Jul. 18, 2001 (“Hidden Patterns”);the contents of both applications are hereby incorporated by referencein their entirety. Software running the KDE is available from CorrelogicSystems, Inc., under the name Proteome Quest™.

[0009] After the KDE is used to generate a control model, a test serummay be compared to the control model to determine if the spectraproduced by the high-throughput bioassay process and the serum areacceptable.

[0010] The KDE will search for hidden or subtle patterns of molecularexpression that are, in and of themselves, “diagnostic.” The level ofthe identified molecular products is termed per se diagnostic, becausethe level of the product is diagnostic without any further considerationof the level of any other molecular products in the sample.

[0011] In the data cluster analysis utilizing the KDE, the diagnosticsignificance of the level of any particular marker, e.g., a protein ortranscript, is a function of the levels of the other elements that areused to calculate a sample vector. Such products are referred to as“contextual diagnostic products.” The KDE's learning algorithm discoverswholly new classification patterns without knowing any prior informationabout the identity or relationships of the data pattern, i.e., withoutprior input that a specified diagnostic molecular product is indicativeof a particular classification.

[0012] If the spectrum produced by the biochip and the serum map to thecontrol model, then the data obtained from mass spectrometry of theserum and biochip may be used for further analysis. If the spectrumproduced by the biochip and the serum fail to map to the control model,the data is deemed uncertified, and new data must be acquired.Alternatively, if a spectrum produced by a serum sample and a diluentmap to the control model, then the spectrum obtained from anelectrospray process may be used for further analysis. By using thismethod, inconsistencies across bioassay processes may be avoided,thereby improving the reliability of data obtained using the bioassayprocess. Other advantages may also be realized from the methodsdisclosed herein, as would be obvious to the ordinarily skilled artisan.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013]FIG. 1 is a flow chart illustrating a method of obtaining acontrol model.

[0014]FIG. 2 depicts a table having various serum/biochip combinationsthat may be used to obtain the control model.

[0015]FIG. 3 illustrates a method of comparing the test serum to thecontrol model.

[0016]FIG. 4 is a depiction of mapping of exemplary tolerances inthree-dimensional space according to one aspect of the presentinvention.

[0017]FIG. 5 is a table illustrating results obtained from the KDE usingtwo different types of biochips and 256 sera.

[0018]FIG. 6 is a flow chart of an alternative embodiment of the presentinvention for use with an electrospray process.

DETAILED DESCRIPTION

[0019] Generally, the invention includes a method of obtaining a controlmodel for use in a bioinformatics system and a method for comparing atest sample against the model for the purpose of QA/QC.

[0020] A method of obtaining a control model according to one aspect ofthe present invention is illustrated in FIG. 1. To ensure a highlyarticulate model, a variety of serum samples are selected at step 100.The selection should include selecting serum from as diverse a group ofindividuals as possible. The more diverse the selected sera, the morearticulate the control model will be. For example, sera may be takenfrom healthy males, healthy females, males afflicted with variousdiseases, females afflicted with various diseases, persons of differentages, and persons of different races.

[0021] Once the diverse group of sera has been selected, a group ofdifferent biochips is selected at step 110. The diverse group ofbiochips may include an anionic chip, a cationic chip, and animmobilized metal chip. The selection of chips may include at least oneanionic chip and at least one cationic chip. However, in order togenerate a workable model at least two chips should be selected. Forexample, one model could be generated using three types of chips: WCX2(cationic exchange), SAX2 (anionic exchange), and IMAC3 (immobilizedmetal) surface enhanced laser desorption/ionization (“SELDI”) chips.

[0022] After the initial selection of sera (100) and the selection ofbiochips (110), the sera are applied to the chips in step 120. Aftereach serum is applied to the surface of a chip, then each chip and seracombination is analyzed by mass spectrometry at step 130 to obtain aspectral output characterized by mass to charge (m/z) values. After onespectrum is produced, the process is repeated for a differentbiochip/serum combination. Each time a spectrum is obtained for aparticular biochip/serum combination, a determination is made at step140 of whether all chips have been analyzed.

[0023] After all of the chips have been analyzed, a determination ismade at step 150 of whether all sera have been analyzed by massspectrometry in combination with each chip type. For example, assumethree sera are selected at step 100, and two biochips are selected (onecationic exchange biochip (“Biochip A”) and one anionic exchange biochip(“Biochip B”)). After the first serum is analyzed by mass spectrometryon Biochip A, a determination is made at step 140 of whether allbiochips have been used. Finding that the first serum has not been usedwith Biochip B, the process is repeated starting with step 120.

[0024] When both Biochip A and Biochip B have been analyzed with thefirst serum, a determination is made at step 150 of whether there areany more sera remaining. If any more sera remain, the process isrepeated for each of the biochips. In this example, the process will berepeated for each of Biochip A and Biochip B, with the second and thirdsera respectively.

[0025] The data for each of the spectra may be stored, such as in thetable illustrated in FIG. 2. The table includes data for “i” sera and“j” chips. Each cell in the table contains mass spectra (MS) dataassociated with each chip type and the various types of serum. Forexample, cell MS_(j,i) corresponds to the spectral data from chip “j”and serum sample “i”. After all of the data have been obtained, thestored mass spectrum data can be input into the KDE to discover hiddenpatterns in the spectral data.

[0026] Next, the process of developing a biochip model using the KDEwill be described.

[0027] The data from each of the mass spectra are input into the KDE.The KDE then seeks to identify clusters of data (hidden patterns) inn-dimensional space, where n is the number of mass to charge valuesselected from the spectra for analysis, and each spectrum can be mappedinto the n-dimensional space using the magnitude of each of the selectedmass to charge values in the spectrum (the combination of a mass tocharge value and the magnitude of the spectrum at that value being avector). The KDE seeks clusters that contain as many of the spectra aspossible and that distinguish each of the biochips from the others. Eachcluster of data will define a centroid that will be associated with aparticular biochip. In the event that a number of possible groupings orcombinations of clusters are identified by the KDE, the user will selectthe most optimal grouping to define the biochip model. The selectionprocess could be automated rather than being directly performed by theuser. In either case, the cluster with the highest population of vectorscan be identified by either the user or the system and that cluster canbe designated as the control model.

[0028] After the model has been obtained, test sera may be run againstthe model to determine if the sera/biochip combination is certified forfurther analysis. One method of QA/QC using the biochip model obtainedin FIG. 1 is depicted in FIG. 3.

[0029] First, in step 300 multiple samples from a test serum is appliedto a biochip. The test serum could be serum intended for a cancerscreening, for example. Then in step 310 the test serum samples areanalyzed by mass spectrometry. The spectra obtained in step 310 are thenmapped to the biochip model in step 320.

[0030] Mapping the spectrum to the biochip model is performed in mannersimilar to the mapping of a serum sample to a training data set todiagnose a particular disease state as described in the Hidden Patternsapplication. Mapping a spectrum to the biochip model includesdetermining the spectrum value for each of the n mass to charge ratiosin the biochip mode, plotting the associated vectors in the model'sn-dimensional space, and comparing them with the location of the model'scentroid. The centroid is defined as the center of the clusterdetermined to have the highest population of vectors from the modeldevelopment.

[0031] After the spectrum of the test sample is mapped to the biochipmodel, it is determined in a step 330 if the spectrum maps within apredetermined hypervolume centered on the centroid in the modelassociated with the tested biochip.

[0032] If the spectrum maps within the predetermined hypervolume, thespectrum is deemed certified for further analysis. If the map of thespectrum falls outside the predetermined hypervolume, the spectrum isnot deemed certified and the sample must be reanalyzed.

[0033] A system employing the method of the present invention shouldoperate within predetermined tolerances. In determining whether aspectrum for a sample maps to the model for the biochip used with thesample, the point to which the vectors from each sample spectrum maps inthe model's n-dimensional space maps are compared to the location of thecentroid for the cluster associated with the selected biochip. Thespectrum is considered to map to the model if it lies within apredetermined hypervolume centered on that centroid. In this embodiment,the hypervolume defined with the centroid as its center will excludeapproximately 95% of the total hypervolume of the n-dimensional space.The content of a polytope or other n-dimensional object is itsgeneralized volume (i.e., its “hypervolume”). Just as athree-dimensional object has volume, surface area and generalizeddiameter, an n-dimensional object has “measures” of order 1, 2, . . . ,n, the hypervolume is defined based on these measures of order. Thehypervolume can also be defined in terms of a predefined acceptableprocess tolerance. The n-dimensional hypervolume calculation is akin toMahalanobis distances used in establishing rejection and acceptancecriteria.

[0034] This can be visualized in three dimensions as depicted in FIG. 4.FIG. 4 illustrates a centroid “C,” which is associated with the clusterof features plotted in n-dimensional space (here the space is depictedas three-dimensional for visualization purposes). A theoretical sphere,“S” is located in the n-dimensional space. The sphere is centered at thelocation of the centroid “C.” Tolerances should be set such that thesphere “S” has a volume that excludes approximately 95% of the volume ofthe three-dimensional space. The volume of the three dimensional spaceis defined by the set of plotted features in that space, and is boundedby (and preferably normalized to) the m/z feature with the greatestintensity.

[0035] Referring back to FIG. 3, once a sample is deemed certified, thespectral data may be used for further analysis. The further analysis mayentail running the data through the KDE to discover hidden patterns inthe spectra. These hidden patterns may be compared to disease statemodels to diagnose for a particular disease. This method of diagnosis isdescribed in further detail in the Hidden Patterns application.

[0036] If no spectra map to the model, then in step 350 the sample andbiochip are deemed non-certified. If the sample and the chip were deemednon-certified the for the first time using a particular serum sample,then in steps 360 and 370 a determination is made to see if the “nocertification” is a first “no certification”; is so, a new chip isselected and the process repeated.

[0037] If the sample and the biochip were determined to be non-certifiedmore than once, then in step 380 a new serum sample and chip areobtained, and the process is repeated. After a certified sample has beenobtained, at step 410 it is output for further analysis.

[0038] In one exemplary test, sera was obtained from subjects with andwithout cancer. Two types of biochips were selected, IMAC3 (immobilizedmetal) and WCX2 (cationic exchange). A diverse group of 256 sera wereselected. The sera were then applied to each type of chip, analyzed bymass spectrometry, and the spectral output collected. The spectral datawas input into the KDE.

[0039] The model identified by the KDE is shown in FIG. 5. The table inFIG. 5 shows the constituent “patterns” or clusters comprising themodel. Each cluster corresponds to a point, or node, in theN-dimensional space defined by the N m/z values (or “features”) includedin the model. In this case, 10 m/z values are included in the model, son=10. The table shows the constituent centroids of the mode, each in arow identified by a “Node” number. Thus, this model has eight nodes orcentroids. The table also includes columns for the constituent featuresor vectors of the centroids, with the m/z value for each vectoridentified at the top of the column. The amplitudes are shown for eachfeature or m/z value, for each centroid, and are normalized to 1.0.

[0040] The remaining four columns in the table are labeled “Count,”“State,” “StateSum,” and “Error.” “Count” is the number of samples inthe Training set that correspond to the identified node. “State”indicates the state of the node, where 1 indicates the IMAC3 chip and 0indicates the WCX2 chip. “StateSum” is the sum of the state values forall of the correctly classified members of the indicated node, while“Error” is the number of incorrectly classified members of the indicatednode. Thus, for node 2, 108 samples were assigned to the node, whereas104 samples were actually from the IMAC3 chip. StateSum is thus 104(rather than 108) and Error is 4.

[0041] The cluster that contained the highest population of vectors wasdesignated as the control model. In the Example illustrated in FIG. 5,node 2 defined the control model because it contained the greatestnumber of vectors (108).

[0042] While the method described above has been described for use withbiochips, another embodiment of the invention may be practiced usingelectrospray techniques to obtain data relating to a particular serum.

[0043] When using an electrospray technique for obtaining biologicaldata for diagnostic purposes, a primary factor limiting the consistencyof the data obtained is the particular diluents used in preparing thesample for electrospray analysis. Therefore, rather than characterizingthe serum in conjunction with the biochips being used, electrospraytechniques used in combination with the present methods willcharacterize the serum in combination with the diluents used.

[0044] To use electrospray ionization (“ESI”) to obtain accuratespectral data, a stable spray should be obtained. There are threephysical characteristics of the spectral data that yield spectralresults that can be utilized by the KDE. These physical characteristicsinclude: (1) the number of mass peaks; (2) the total ion current; and(3) the stability of the spray.

[0045] Various tests were run to determine a preferred diluentconcentration and composition to yield effective results for use in theKDE. These tests were run using electrospray apparatus manufactured byAdvion BioSciences, and particularly the NanoMate100™ ESI in conjunctionwith Correlogic's Proteome Pattern Blood Test™ which is based on theProteome Quest™ software.

[0046] One particular test involved diluting the serum sample at 1:1000in 50:50 acetonitrile: H₂O containing 0.2% formic acid (FA). While 20 λof each sample was aliquoted into each well, representing 0.02 λ ofserum, only about 100-200 nano-liters was actually sprayed and analyzed.Operating the Q Star mass spectrometer in positive ion TOF/MS mode, datawas acquired for 2 minutes/sample, m/z of 300-2000, using a 1 secondscan rate. The nanospray was initiated by applying 1.55 kV spray voltageat a pressure of about 0.5 psi.

[0047] Based on testing of serum samples a determination was made thatthe Multichannel acquisition mode (MCA) should be used on the Q-starmass spectrometer when obtaining spectral data. This is based on adetermination that the MCA mode produced better resolution of thespectral peaks.

[0048] To optimize the diluent used in preparing the serum samples,various other tests were run. The concentrations of the serum to diluentare preferably between 1:1000 and 1:250, but other diluentconcentrations may be selected in a manner apparent to those skilled inthe art. Two diluent types tested included acetonitrile (ACN) andmethanol (MeOH). Each of these diluents was combined with an acid. Acidsinclude, but are not limited to, trifluoroacetic acid (TFA), formic acid(FA), and acetic acid. Either TFA or FA can be used for purposes of thepresent invention and are preferably in concentrations between about0.2% acid and 1.0% acid.

[0049] An alternative embodiment of the method of obtaining a modelaccording to the present invention will now be described in relation toFIG. 6. The step of selecting sera in step 100 is the same as describedabove. Again, the more diverse the overall group of selected sera is,the more articulate the model will be.

[0050] Secondly, in step 610 a selection of diluents is made. Diluentsselected may be diverse or homogeneous. For example, a diverse group ofdiluents including ACN and MeOH may be selected. Alternatively, only ACNmay be selected as long as the concentrations of the ACN differ (e.g.,1:1000, 1:500, and 1:250 serum to ACN).

[0051] Thirdly, in step 620, a mixture is made according topredetermined concentrations of serum to diluent. This mixture is thenanalyzed using electrospray in step 630 to obtain spectral data. Thisprocess is repeated for all desired concentrations, diluents and serumsamples until all data have been obtained in steps 640, 650, and 660. Amodel is then obtained in step 670 based on the data extracted from thevarious samples. The model is obtained using the KDE in the same manneras described above for the acquisition of a biochip model.

[0052] A method of QA/QC using the electrospray is substantially thesame as for the disclosed method of QA/QC for biochips. Notablevariations can be found in the generation of the sample and the methodof obtaining the data. The significant differences between the overallprocesses stems from the differences in obtaining the model and itsability to identify a particular serum diluent.

[0053] In one particular test, sera were obtained from male and femalesubjects. Two diluents, acetonitrile (ACN) and methanol (MeOH) wereselected. A mixture was made at a concentration of 1:250 of serum todiluent. Selected sera mixture (102 samples) were analyzed byelectrospray mass spectrometry, and the spectral output collected. Thespectral data was input into the KDE. The KDE identified a modelcontaining three clusters in total that distinguished the two diluents.One cluster was associated with ACN and that cluster was designated asthe control model.

[0054] The above-described method is applicable to various bioassayprocesses to ensure that both the particular high-throughput bioassayprocess being used and the serum being tested will yield an accuratediagnostic. By using the method described above, biological diagnosticsmay be provided that have increased accuracy and reliability.

[0055] Tolerances to employ the aforementioned methods were described asbeing such that a hypervolume defined about the centroid of a clusterthat will exclude approximately 95% of the total hypervolume of then-dimensional space. While 95% percent was explicitly mentioned, one ofordinary skill in the art would realize that the methods of the presentinvention would operate effectively with different sized hypervolumescentered on the centroid.

[0056] In the described embodiments, biochips and electrospray processeswere illustrative. Various other high-throughput bioassay processes areknown in the art and could be employed with the methods of the presentinvention.

[0057] In the described embodiments, to obtain a model characteristic ofa particular high-throughput bioassay process, sera should be taken fromhealthy males, healthy females, males afflicted with a disease, femalesafflicted with a disease, persons of different ages and persons ofdifferent races. While these specific examples were given, numerousother diverse sera samples could be taken. The best possible diversesera would contain serum from every individual in the world. Therefore,taking sera from any individual that does not group into one of theaforementioned classifications is within the scope of the presentinvention.

[0058] While specific diluents and acids were described in reference tothe methods of QA/QC for electrospray techniques, these diluents andacids are not intended to be exhaustive and a variety of other suitablediluents and acids are suitable for those explicitly mentioned.Additionally, while specific concentrations of both acids and ratios ofsera to diluent were disclosed, one of ordinary skill in the art willrealize the specific concentrations will depend on the particular acidsand diluents used to perform the inventive method, and the describedacids and diluents are not intended to be all inclusive. Various otherconcentrations in combination with various acids and diluents will beobvious to the ordinarily skilled artisan based on the teachings of thepresent invention.

[0059] The various features of the invention have been described inrelation to a method of quality assurance/quality control ofhigh-throughput bioassay processes. However, it will be appreciated thatmany of the steps may be implemented with various apparatus andbioinformatics methods. Moreover, variations and modifications existthat would not depart from the scope of the invention.

We claim:
 1. A method, comprising: selecting a diverse group of sera,the diverse group of sera having different characteristics; dilutingeach serum of the diverse group of sera with a plurality of differentdiluents; obtaining information associated with a mass spectrum of eachof the diluted sera from the diverse group of sera using an electrosprayprocess; generating a control model based at least in part on thespectrum obtained from the diverse group of sera; diluting a test serumwith a test diluent; performing mass spectrometry on the test serum toobtain a test spectrum associated with the test serum; mapping the testspectrum obtained from said performing to the control model; determiningwhether the test spectrum obtained from said performing maps to thecontrol model.
 2. The method of claim 1, said generating furthercomprising: selecting a cluster that contains the greatest number ofvectors from the spectra to define the control model.
 3. The method ofclaim 1, wherein said diluting each serum of the diverse group of seraincludes diluting the sera with diluents having a predetermined diluentconcentration, and said diluting a test serum with a test diluentincludes diluting a test serum with a diluent having the sameconcentration as the diluent used to dilute each serum of the diversegroup of sera.
 4. The method of claim 1, wherein said diluting eachserum of the diverse group of sera includes diluting the sera withdiluents having a predetermined diluent concentration, and said dilutinga test serum with a test diluent includes diluting a test serum with adiluent having a different concentration than the diluent used to diluteeach serum of the diverse group of sera.
 5. The method of claim 1,further comprising: classifying a biological state from the spectrumbased on a predetermined biological state model.
 6. The method of claim1, wherein if said determining determines that the spectrum does not mapto the control model, and the diluent is a first diluent, the methodfurther comprising: repeating the steps of diluting, performing,mapping, and determining for a second diluent.
 7. The method of claim 1,said selecting further comprising: selecting at least two different serafrom a pool of diverse sera, the pool of diverse sera consisting of:sera from healthy males, sera from healthy females, sera from malesafflicted with a disease, sera from females afflicted with a disease,sera from persons of different races, and sera from people of differentages.
 8. The method of claim 1, wherein said generating includes:identifying at least one cluster in common to the sera of the diversegroup of sera and the plurality of different diluent; and selecting onlyone cluster as part of the control model.
 9. The method of claim 1,wherein the obtaining information includes: obtaining information onsera diluted with two different diluents, the diluents including atleast acetonitrile and methanol.
 10. The method of claim 1, wherein thetest diluent is one of the plurality of different diluents.
 11. Themethod of claim 1, wherein the test diluent is not one of the pluralityof different diluents.
 12. A method of quality assurance employing acontrol model generated based on mass spectra obtained from seraanalyzed following an electrospray process, the spectra being associatedwith a plurality of different sera and a plurality of differentdiluents, comprising: diluting a serum using a diluent; ionizing thediluted serum using an electrospray ionization process; performing massspectrometry on the ionized diluted serum to obtain spectral dataassociated with the serum and the diluent; and mapping the spectrum tothe control model, said mapping being performed to determine if theserum and the diluent are suitable for further diagnostics.
 13. Themethod of claim 12, further comprising: determining that the serum anddiluent are suitable for further diagnostics; and submitting thespectral data to a biological model to determine if the biologicalsample exhibits a particular biological state.
 14. The method of claim13, wherein diluting a serum includes diluting a serum using one ofacetonitrile and methanol.
 15. A method, comprising: diluting at leasttwo sera of a diverse group of sera with a diluent having a plurality ofdifferent concentrations to yield a plurality of diluted sera samples,the plurality of diluted sera samples having different concentrations ofserum to diluent; ionizing at least some of the plurality of dilutedsera samples using an electrospray ionization process to yield aplurality of ionized diluted sera; obtaining spectral data associatedwith the ionized diluted sera; generating a control model associatedwith the diluted sera samples; diluting a test serum with a diluent toyield a diluted test serum; ionizing the diluted test serum using theelectrospray ionization process to yield an ionized diluted test serum;obtaining spectral data associated with the diluted test serum; mappingthe spectral data associated with the diluted test serum to the controlmodel; and determining whether the diluted test serum produces aspectrum within a predetermined tolerance.
 16. The method of claim 15,wherein said diluting at least two of a diverse group of sera includesdiluting a diverse group of sera using at least one of acetonitrile andmethanol.
 17. The method of claim 15, wherein said diluting at least twosera of a diverse group sera includes creating a plurality of dilutionsof the at least two of the plurality of diverse group of sera with adiluent having a plurality of concentrations.
 18. The method of claim15, wherein said diluting at least two sera of a diverse group of seraincludes creating a plurality of dilutions of the at least two of theplurality of diverse group of sera with a diluent having a plurality ofconcentrations, the concentrations ranging between 1:250 to 1:1000. 19.The method of claim 15, wherein said generating the control modelincludes: determining the location of at least one cluster inn-dimensional space; and selecting a cluster having the greatest numberof vectors within the cluster to define the control model.
 20. Themethod of claim 15, wherein said diluting the test serum includesdiluting the test serum with a known diluent.
 21. The method of claim15, wherein said diluting the test serum with a diluent includesdiluting the test serum with the same diluent used to dilute the atleast two sera of a diverse group of sera.
 22. The method of claim 15,wherein said diluting the test serum with a diluent includes dilutingthe test serum with a different diluent than the diluent used to dilutethe at least two sera of a diverse group of sera.
 23. The method ofclaim 15, wherein the test diluent is one of the plurality of differentdiluents.
 24. A method, comprising: diluting a first serum and a secondserum, the first serum having different properties from the secondserum, the first serum and the second serum being diluted with a diluentto produce a diluted first serum and a diluted second serum; ionizingthe diluted first serum using an electrospray ionization process;obtaining spectral data associated with the diluted first serum;ionizing the diluted second serum using an electrospray ionizationprocess; obtaining spectral data associated with the diluted secondserum; mapping the spectral information obtained from the diluted firstserum and the diluted second serum into n-dimensional space; generatinga control model based on said mapping, the control model being based onthe diluted first serum and the diluted second serum; diluting a testserum with a diluent to yield a diluted test serum; ionizing the dilutedtest serum using the electrospray ionization process to yield an ionizeddiluted test serum; obtaining spectral data associated with the dilutedtest serum; mapping the spectral data associated with the diluted testserum to the control model; and determining whether the diluted testserum produces a spectrum that satisfies predetermined criteria.
 25. Themethod of claim 24, wherein said ionizing the diluted first serum andionizing the diluted second serum are ionized by the same electrosprayionization process.
 26. The method of claim 24, wherein said determiningwhether the diluted test serum produces a spectrum that satisfiespredetermined criteria includes: identifying whether the spectrum iswithin one of a first hypervolume and a first volume such that one ofthe first volume and the first hypervolume excludes at least 90% of oneof a second hypervolume and a second volume, the one of a secondhypervolume and a second volume being the total volume of hypervolume ofan n-dimensional space.